An online classification EM algorithm based on the mixture model

Mixture model-based clustering is widely used in many applications. In certain real-time applications the rapid increase of data size with time makes classical clustering algorithms too slow. An online clustering algorithm based on mixture models is presented in the context of a real-time flaw-diagnosis application for pressurized containers which uses data from acoustic emission signals. The proposed algorithm is a stochastic gradient algorithm derived from the classification version of the EM algorithm (CEM). It provides a model-based generalization of the well-known online k-means algorithm, able to handle non-spherical clusters. Using synthetic and real data sets, the proposed algorithm is compared with the batch CEM algorithm and the online EM algorithm. The three approaches generate comparable solutions in terms of the resulting partition when clusters are relatively well separated, but online algorithms become faster as the size of the available observations increases.     

Inferring an Augmented Bayesian Network to Confront a Complex Quantitative Microbial Risk Assessment Model with Durability Studies: Application to Bacillus Cereus on a Courgette Purée Production Chain

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts’ knowledge about the microbial dynamics of a given food‐borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high‐dimensional second‐order QMRA model. The case study is a farm‐to‐fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time‐temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second‐order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.     

Global Sensitivity Analysis Applied to a Contamination Assessment Model of Listeria monocytogenes in Cold Smoked Salmon at Consumption

In this study, a variance‐based global sensitivity analysis method was first applied to a contamination assessment model of Listeria monocytogenes in cold smoked vacuum packed salmon at consumption. The impact of the choice of the modeling approach (populational or cellular) of the primary and secondary models as well as the effect of their associated input factors on the final contamination level was investigated. Results provided a subset of important factors, including the food water activity, its storage temperature, and duration in the domestic refrigerator. A refined sensitivity analysis was then performed to rank the important factors, tested over narrower ranges of variation corresponding to their current distributions, using three techniques: ANOVA, Spearman correlation coefficient, and partial least squares regression. Finally, the refined sensitivity analysis was used to rank the important factors.     

The Good,the Bad and the Ugly: Discourse,Policy Controversies and the Role of Science in the Politics of Shrimp Farming Development

This article revisits through a policy analysis the ongoing debate on shrimp farming aquaculture. It describes the changes in policy orientations that have taken place in recent years, and tries to relate them to the advocacy strategies developed by different networks and policy communities. The analysis reveals in particular the crucial contribution of the ‘power of expertise’ and shows how it has been instrumentalised by certain advocacy networks to depoliticise the debate. While this has allowed a number of key stakeholders to refocus the debate on technical solutions, it has prevented other groups concerned with more intractable social and political issues from engaging successfully in the policy process, thus leaving the long‐term sustainability of aquaculture still a contentious issue.     

Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. Indeed, experts can qualitatively assess the potential of each cluster, and with appropriate statistical methods, these qualitative assessments can be quantified into a success probability for each of them. However, one crucial element often overlooked is the procedure by which the clusters are assigned to/selected by the experts for evaluation. In the present work, the impact such a procedure may have on the statistical analysis and the entire evaluation process is studied. It has been shown that some implementations of the selection procedure may seriously compromise the validity of the evaluation even when the rating and selection processes are independent. Consequently, the fully random allocation of the clusters to the experts is strongly advocated. Copyright © 2014 John Wiley & Sons, Ltd.     

Infants' Acquisition of Grammatical Gender Dependencies

To successfully understand spoken language, listeners need to determine how words within sentences relate to one another. Although the ability to compute relationships between word categories is known to develop early in life, little research has been conducted on infants' early sensitivity to subcategorical dependencies, such as those evoked by grammatical gender (where the article form is dictated by the noun's gender). This study therefore examines whether French‐learning 18‐month‐olds track such relationships. Using the Visual Fixation Procedure, infants were presented with article–noun sequences in which the gender‐marked article either matched (e.g., la_FEM poussette_FEM “the stroller”) or mismatched (e.g., le_MASC poussette_FEM) the gender of the noun. A clear preference for correct over incorrect co‐occurrences was observed, suggesting that by 18 months of age, children's storage and access of words is sufficiently sophisticated to include the means to track subcategorical dependencies. This early sensitivity to gender information may be greatly beneficial for constraining lexical access during online language processing.     

Logistic discrimination using robust estimators: An influence function approach

Logistic regression is frequently used for classifying observations into two groups. Unfortunately there are often outlying observations in a data set and these might affect the estimated model and the associated classification error rate. In this paper, the authors study the effect of observations in the training sample on the error rate by deriving influence functions. They obtain a general expression for the influence function of the error rate, and they compute it for the maximum likelihood estimator as well as for several robust logistic discrimination procedures. Besides being of interest in their own right, the influence functions are also used to derive asymptotic classification efficiencies of different logistic discrimination rules. The authors also show how influential points can be detected by means of a diagnostic plot based on the values of the influence function     

The Probabilistic Final Standing Calculator: a fair stochastic tool to handle abruptly stopped football seasons

AStA Advances in Statistical Analysis - The COVID-19 pandemic has left its marks in the sports world, forcing the full stop of all sports-related activities in the first half of 2020. Football...     

14‐month‐olds exploit verbs’ syntactic contexts to build expectations about novel words

During their second year of life, infants develop a rudimentary understanding of grammatical categories based on their knowledge and use of frequent function words. The current study inquired whether, at only 14 months of age, infants can track co‐occurrence patterns between function words and content words (e.g., determiners can precede nouns, and pronouns can precede verbs), and use these previously encountered syntactic contexts to build expectations about which function words can co‐occur with novel words. Using a habituation paradigm, French‐learning 14‐month‐olds were presented with utterances containing two novel words preceded by function words (either two determiners in the Novel Nouns condition or two pronouns in the Novel Verbs conditions). We found that at test, infants looked longer during trials in which the novel words occurred in an unexpected syntactic context (following a pronoun for infants in the Novel Nouns condition and following a determiner for infants in the pooled analysis of the three Novel Verbs conditions). Hence, our results confirm previous findings on infants’ sensitivity to noun contexts and most importantly demonstrate that their sensitivity to the co‐occurrence of verbs with pronouns begins much earlier than previously understood.     

On temperance and risk spreading

Theory and Decision - On page 599 in the original publication of the article.